摘要
提出了一种对象级的保边缘影像平滑算法。该算法利用空间聚类对影像进行多尺度分割,在分割过程中,提取出不同尺度下的符合凸面模型(convexity model)的影像对象(image object);依据对象的统计参数对影像对象进行筛选,符合要求的影像对象内部进行平滑处理,其余对象不受影响。利用该方法可以有效地去除噪声和无用小目标,在不破坏指定目标边缘的同时,实现影像的平滑处理。
In image process, smoothing process will effect object edges extraction. In remote sensing image application, it＇s one of the difficulties to implement smoothing image and ensure the accuracy of edge extraction for specific objects at the same time. In this paper, we propose an object-based and edge-preserve image smoothing algorithm which divides image into many image objects with multi scales by using spatial clustering. In the course of segmentation, all image objects which accord with the Convexity Model will be extracted. Based on the prior object-specified statistical parameters, only those meet the requirements will be smooihed and other ones will be unaffected. Without damaging the edges of specified objects, our method can effectively remove noises and insignificant objects.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2009年第4期423-426,487,共5页
Geomatics and Information Science of Wuhan University
基金
国家863计划资助项目(2006AA12A115)
国家自然科学基金资助项目(60602013)
关键词
面向对象
凸面模型
多尺度分割
object-oriented
convexity model
multi-scale image segmentation
